# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# ==============================================================================
"""Contains a variant of the LeNet model definition."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

slim = tf.contrib.slim


def lenet(images, num_classes=10, is_training=False,
          dropout_keep_prob=0.5,
          prediction_fn=slim.softmax,
          scope='LeNet'):
    """Creates a variant of the LeNet model.
  
    Note that since the output is a set of 'logits', the values fall in the
    interval of (-infinity, infinity). Consequently, to convert the outputs to a
    probability distribution over the characters, one will need to convert them
    using the softmax function:
  
          logits = lenet.lenet(images, is_training=False)
          probabilities = tf.nn.softmax(logits)
          predictions = tf.argmax(logits, 1)
  
    Args:
      images: A batch of `Tensors` of size [batch_size, height, width, channels].
      num_classes: the number of classes in the dataset.
      is_training: specifies whether or not we're currently training the model.
        This variable will determine the behaviour of the dropout layer.
      dropout_keep_prob: the percentage of activation values that are retained.
      prediction_fn: a function to get predictions out of logits.
      scope: Optional variable_scope.
  
    Returns:
      logits: the pre-softmax activations, a tensor of size
        [batch_size, `num_classes`]
      end_points: a dictionary from components of the network to the corresponding
        activation.
    """
    end_points = {}

    with tf.variable_scope(scope, 'LeNet', [images, num_classes]):
        net = slim.conv2d(images, 32, [5, 5], scope='conv1')
        net = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
        net = slim.conv2d(net, 64, [5, 5], scope='conv2')
        net = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
        net = slim.flatten(net)
        end_points['Flatten'] = net

        net = slim.fully_connected(net, 1024, scope='fc3')
        net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                           scope='dropout3')
        logits = slim.fully_connected(net, num_classes, activation_fn=None,
                                      scope='fc4')

    end_points['Logits'] = logits
    end_points['Predictions'] = prediction_fn(logits, scope='Predictions')

    return logits, end_points


lenet.default_image_size = 28


def lenet_arg_scope(weight_decay=0.0):
    """Defines the default lenet argument scope.
  
    Args:
      weight_decay: The weight decay to use for regularizing the model.
  
    Returns:
      An `arg_scope` to use for the inception v3 model.
    """
    with slim.arg_scope(
            [slim.conv2d, slim.fully_connected],
            weights_regularizer=slim.l2_regularizer(weight_decay),
            weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
            activation_fn=tf.nn.relu) as sc:
        return sc
